I went with a feature around personalized recommendations based on past dining behavior, which felt solid but in hindsight I spent too long on the setup and not enough time actually defending the prioritization.
Start by clarifying the goal (e.g., increase user engagement or retention) and identifying a specific user pain point through a structured product sense framework. Then propose a feature that leverages Yelp's unique data and Meta's engineering strengths, and prioritize it using impact vs. effort. Finally, outline how you would measure success and iterate.
Pro tip: Show that you understand Yelp's business model and competitive landscape by tying your feature to a clear metric (e.g., daily active users, review submissions) and explaining how it differentiates Yelp from Google Maps or TripAdvisor.
Ask clarifying questions to understand what 'improve' means (e.g., user growth, engagement, monetization) and which user segment to focus on. This shows you can align with business objectives.
Choose a specific user segment (e.g., diners, reviewers) and articulate a concrete problem they face, such as difficulty discovering personalized recommendations or lack of trust in reviews.
Describe a feature that addresses the pain point, leveraging Yelp's strengths (e.g., rich review data, photos) and Meta's tech (e.g., AI, social graph). Be specific about how it works.
Explain why this feature is high-impact and feasible compared to alternatives, using a framework like RICE (Reach, Impact, Confidence, Effort) or impact vs. effort. Mention trade-offs.
Outline how you would measure success (e.g., increase in reviews, session time) and what you would do to iterate or scale. This shows a results-oriented mindset.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.